not-yet-known not-yet-known not-yet-known unknown Early-Onset Dementia (EOD), which includes Frontotemporal Dementia (FTD), behavioral variant (bvFTD) and Early-Onset Alzheimer’s Disease (EOAD), poses significant diagnostic and therapeutic challenges due to its diverse clinical manifestations and unique neurodegenerative mechanisms. This review integrates findings from studies employing advanced neuroimaging modalities—electroencephalography (EEG), functional MRI (fMRI), Diffusion Tensor Imaging (DTI), and fluorodeoxyglucose positron emission tomography (FDG-PET) analyzed through graph-theoretical frameworks to investigate topological disruptions associated with EOD. Metrics such as global efficiency, local efficiency, hubs, and modularity provide critical insights into the altered topological organization of brain networks in EOD. Graph network analysis reveals paradoxical preservation of small-world properties and compensatory mechanisms in the early stages of EOD, juxtaposed against a progressive decline in network efficiency and modular integration as the disease advances. Traditional metrics like global efficiency and small worldness may oversimplify dynamic compensatory processes and disease-specific network changes. Hallmarks of EOD include hub vulnerability, rich club network disruptions, and modular breakdowns, which reflect the selective impact of tau and amyloid pathologies on brain connectivity. These findings highlight the clinical value of graph-theoretical approaches in differentiating EOD subtypes, tracking disease progression, and guiding therapeutic strategies. Future research should prioritize the standardization of methodologies, longitudinal studies to monitor dynamic network alterations, and a deeper exploration of compensatory mechanisms to enhance early diagnosis and develop targeted interventions for EOD. This approach holds promise for advancing both the understanding and management of this complex condition.